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Advances and Applications in Aerial Unmanned Robots: Sensing, Planning, and Control

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 30 October 2024 | Viewed by 2921

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Special Issue Editors

Prof. Dr. Bo Zhang

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Guest Editor

1. College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
2. Guangdong Artificial Intelligence and Digital Economy Laboratory (Shenzhen), Shenzhen 518107, China
3. Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation, Shenzhen University, Shenzhen 518060, China
Interests: autonomous unmanned system positioning map; path planning and control

Dr. Yue Wei

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Guest Editor

Guangdong Artificial Intelligence and Digital Economy Laboratory (Shenzhen), Shenzhen 518107, China
Interests: collaborative control and optimization of unmanned systems

Dr. Shiyu Chen

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Guest Editor

Guangdong Artificial Intelligence and Digital Economy Laboratory (Shenzhen), Shenzhen 518107, China
Interests: aircraft dynamics and control; underactuated system control theory

Dr. Yu Hu

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Guest Editor

Guangdong Artificial Intelligence and Digital Economy Laboratory (Shenzhen), Shenzhen 518107, China
Interests: serial data processing; multi-sensor integration technology; edge measurement AI intelligent computing platform; cloud distributed machine learning system

Dr. Yaohua Liu

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Guest Editor

Guangdong Artificial Intelligence and Digital Economy Laboratory (Shenzhen), Shenzhen 518107, China
Interests: mobile robot autonomous positioning; unmanned vehicles

Special Issue Information

Dear Colleagues,

As sensor and control technology rapidly advance, aerial unmanned robots have been more widely investigated due to their applications in the areas of surveillance, monitoring, infrastructure inspection, delivery, etc. Aerial unmanned robots include fixed-wing drones, multi-rotor drones, aerostat, etc., which can offer tailored solutions for diverse observation tasks. Research into aerial unmanned robots encompasses the territories of sensing, planning, and control. In complex environments, it may require multiple aerial unmanned robots to complete sensing tasks cooperatively, followed by real-time data fusion. Optimal planning results for aerial unmanned robots must also be obtained through optimization theories in order to address requirements such as energy efficiency and obstacle avoidance. Simultaneously, a dependable control strategy is indispensable for the motion of aerial unmanned robots in perturbed environments like complex wind fields.

You are invited to submit to this Special Issue of Sensors, entitled “Advances and Applications in Aerial Unmanned Robots: Sensing, Planning, and Control”. This publication is dedicated to present the innovative research on concepts, theoretical findings and practical solutions for aerial unmanned robots in the fields of sensing, planning, and control. Topics from this Special Issue include, but not limited to, the following areas:

Kinematic and dynamic modelling for novel aerial robots;
Multiple aerial unmanned robot cooperation;
Distributed sensing technology and data fusion;
Optimal path planning for aerial unmanned robots;
Machine learning for detection, prediction, and control;
Station-keeping control of stratosphere aerostat;
Earth and ocean observation by aerial unmanned robots;
Self-states monitoring of aerial vehicle.

Prof. Dr. Bo Zhang
Dr. Yue Wei
Dr. Shiyu Chen
Dr. Yu Hu
Dr. Yaohua Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

robots
UAV
path planning
observation
perception
sensors

Published Papers (5 papers)

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Research

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20 pages, 3929 KiB

Open AccessArticle

Exploration-Based Planning for Multiple-Target Search with Real-Drone Results

by Bilal Yousuf, Zsófia Lendek and Lucian Buşoniu

Sensors 2024, 24(9), 2868; https://doi.org/10.3390/s24092868 - 30 Apr 2024

Viewed by 321

Abstract

Consider a drone that aims to find an unknown number of static targets at unknown positions as quickly as possible. A multi-target particle filter uses imperfect measurements of the target positions to update an intensity function that represents the expected number of targets. We propose a novel receding-horizon planner that selects the next position of the drone by maximizing an objective that combines exploration and target refinement. Confidently localized targets are saved and removed from consideration along with their future measurements. A controller with an obstacle-avoidance component is used to reach the desired waypoints. We demonstrate the performance of our approach through a series of simulations as well as via a real-robot experiment in which a Parrot Mambo drone searches from a constant altitude for targets located on the floor. Target measurements are obtained on-board the drone using segmentation in the camera image, while planning is done off-board. The sensor model is adapted to the application. Both in the simulations and in the experiments, the novel framework works better than the lawnmower and active-search baselines. Full article

(This article belongs to the Special Issue Advances and Applications in Aerial Unmanned Robots: Sensing, Planning, and Control)

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22 pages, 7518 KiB

Open AccessArticle

Omni-OTPE: Omnidirectional Optimal Real-Time Ground Target Position Estimation System for Moving Lightweight Unmanned Aerial Vehicle

by Yi Ding, Jiaxing Che, Zhiming Zhou and Jingyuan Bian

Sensors 2024, 24(5), 1709; https://doi.org/10.3390/s24051709 - 6 Mar 2024

Viewed by 522

Abstract

Ground target detection and positioning systems based on lightweight unmanned aerial vehicles (UAVs) are increasing in value for aerial reconnaissance and surveillance. However, the current method for estimating the target’s position is limited by the field of view angle, rendering it challenging to fulfill the demands of a real-time omnidirectional reconnaissance operation. To address this issue, we propose an Omnidirectional Optimal Real-Time Ground Target Position Estimation System (Omni-OTPE) that utilizes a fisheye camera and LiDAR sensors. The object of interest is first identified in the fisheye image, and then, the image-based target position is obtained by solving using the fisheye projection model and the target center extraction algorithm based on the detected edge information. Next, the LiDAR’s real-time point cloud data are filtered based on position–direction constraints using the image-based target position information. This step allows for the determination of point cloud clusters that are relevant to the characterization of the target’s position information. Finally, the target positions obtained from the two methods are fused using an optimal Kalman fuser to obtain the optimal target position information. In order to evaluate the positioning accuracy, we designed a hardware and software setup, mounted on a lightweight UAV, and tested it in a real scenario. The experimental results validate that our method exhibits significant advantages over traditional methods and achieves a real-time high-performance ground target position estimation function. Full article

(This article belongs to the Special Issue Advances and Applications in Aerial Unmanned Robots: Sensing, Planning, and Control)

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24 pages, 12639 KiB

Open AccessArticle

Cooperative Safe Trajectory Planning for Quadrotor Swarms

by Yahui Zhang, Peng Yi and Yiguang Hong

Sensors 2024, 24(2), 707; https://doi.org/10.3390/s24020707 - 22 Jan 2024

Viewed by 647

Abstract

In this paper, we propose a novel distributed algorithm based on model predictive control and alternating direction multiplier method (DMPC-ADMM) for cooperative trajectory planning of quadrotor swarms. First, a receding horizon trajectory planning optimization problem is constructed, in which the differential flatness property is used to deal with the nonlinear dynamics of quadrotors while we design a relaxed form of the discrete-time control barrier function (DCBF) constraint to balance feasibility and safety. Then, we decompose the original trajectory planning problem by ADMM and solve it in a fully distributed manner with peer-to-peer communication, which induces the quadrotors within the communication range to reach a consensus on their future trajectories to enhance safety. In addition, an event-triggered mechanism is designed to reduce the communication overhead. The simulation results verify that the trajectories generated by our method are real-time, safe, and smooth. A comprehensive comparison with the centralized strategy and several other distributed strategies in terms of real-time, safety, and feasibility verifies that our method is more suitable for the trajectory planning of large-scale quadrotor swarms. Full article

(This article belongs to the Special Issue Advances and Applications in Aerial Unmanned Robots: Sensing, Planning, and Control)

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20 pages, 4067 KiB

Open AccessArticle

Research on Lateral Safety Spacing for Fusion Operation Based on Unmanned and Manned Aircraft-Event Modeling

by Chao Zhou, Chi Huang, Longyang Huang, Chuanjiang Xie, Xingyu Zhu and Tao Huang

Sensors 2024, 24(2), 553; https://doi.org/10.3390/s24020553 - 16 Jan 2024

Viewed by 670

Abstract

With the rapid development of unmanned aerial vehicle technology and its increasing application across various fields, current airspace resources are insufficient for unmanned aerial vehicles’ needs. This paper, taking Zigong General Aviation Airport in Sichuan as a case study, explores the lateral safety spacing in a mixed operation mode of unmanned aerial vehicles and manned aircraft. Currently, there are no standardized regulations for the safe spacing of the fusion operation of unmanned and manned aircraft. Theoretical research is essential to provide a reference for actual operations. It introduces the UM-Event (unmanned and manned aircraft-event) collision risk model, an adaptation of the Event collision risk model, considering factors like communication, navigation, surveillance performance, human factors, collision avoidance equipment performance, and meteorology. Safety spacing was determined via simulation experiments and actual data analysis, adhering to the target safety level (TSL). Findings indicate that surveillance performance has a minor impact on safety spacing, while communication and navigation significantly influence it. The safety spacing, influenced solely by CNS (communication performance, navigation performance, surveillance performance) and combined factors, increased from 4.42 to 4.47 nautical miles. These results offer theoretical guidance for unmanned aerial vehicle safety in non-segregated airspace. Full article

(This article belongs to the Special Issue Advances and Applications in Aerial Unmanned Robots: Sensing, Planning, and Control)

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Review

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24 pages, 2438 KiB

Open AccessReview

Visual SLAM for Unmanned Aerial Vehicles: Localization and Perception

by Licong Zhuang, Xiaorong Zhong, Linjie Xu, Chunbao Tian and Wenshuai Yu

Sensors 2024, 24(10), 2980; https://doi.org/10.3390/s24102980 - 8 May 2024

Viewed by 250

Abstract

Localization and perception play an important role as the basis of autonomous Unmanned Aerial Vehicle (UAV) applications, providing the internal state of movements and the external understanding of environments. Simultaneous Localization And Mapping (SLAM), one of the critical techniques for localization and perception, is facing technical upgrading, due to the development of embedded hardware, multi-sensor technology, and artificial intelligence. This survey aims at the development of visual SLAM and the basis of UAV applications. The solutions to critical problems for visual SLAM are shown by reviewing state-of-the-art and newly presented algorithms, providing the research progression and direction in three essential aspects: real-time performance, texture-less environments, and dynamic environments. Visual–inertial fusion and learning-based enhancement are discussed for UAV localization and perception to illustrate their role in UAV applications. Subsequently, the trend of UAV localization and perception is shown. The algorithm components, camera configuration, and data processing methods are also introduced to give comprehensive preliminaries. In this paper, we provide coverage of visual SLAM and its related technologies over the past decade, with a specific focus on their applications in autonomous UAV applications. We summarize the current research, reveal potential problems, and outline future trends from academic and engineering perspectives. Full article

(This article belongs to the Special Issue Advances and Applications in Aerial Unmanned Robots: Sensing, Planning, and Control)

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